Advancing the Modelica™ Ocean Engineering Toolbox With the Capability to Generate Accurate Wave Excitation Forces
Bibliographic record
Abstract
Abstract The Ocean Engineering Toolbox (OET) is an open-source, Modelica™ compliant symbolic library capable of simulating the dynamic motions of floating bodies subject to polychromatic, unidirectional waves. This paper presents recent advancements in the OET: (1) the development of custom components to represent the frequency-dependent wave excitation force and (2) significant improvements in the toolbox’s computational performance. While the Modelica language can represent complex cyber-physical systems efficiently, the primary challenges with modeling wave-induced floating bodies are (1) non-compatibility with frequency-dependent variables, (2) lack of hydrodynamic components in the Modelica Standard Library (MSL), and (3) integration with the previous version of the OETv0.1. In the time-domain implementation, surface elevation profiles can now be generated for regular and irregular waves, with three spectral options for polychromatic seas: Pierson-Moskowitz (PM), Bretschneider, and Joint North Sea Wave Project (JONSWAP). Hydrodynamics coefficients are imported using a MATLAB script to process the output from Boundary Element Method (BEM) codes. A novel symbolic implementation of spectral decomposition and interpolation is presented in this work to generate the frequency-dependent excitation force in Modelica. Results from the OET are compared against data generated from WEC-Sim, validating both the wave excitation force and the corresponding system dynamic response for a floating point absorber in heave. These developments lead to superior simulation accuracy and computational performance while expanding the hydrodynamic modeling capabilities of Modelica.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".